Writing a
Data Management Plan
for Grant Applications

Vicky Steeves & Nick Wolf

What is Data Management?

Managing the way data is collected, processed, analyzed, preserved, and published for greater reuse by the community and the original researcher.

What is Data?

"the recorded factual material commonly accepted in the scientific community as necessary to validate research findings." -Federal Office of Management & Budget Circular A-110

Federal Regulations

The Problem...

The majority of federally funded research is NOT reproducible.

Disappearing Data

The Solution


a document that describes how you will collect, organise, manage, store, secure, backup, preserve, and share your data.

High-Level View of RDM

Data Type Group Roles Data Storage Data Archiving
format of data to be generated who is primarily responsible for carrying out RDM? Set group norms where will you store your data and how will you backup your data? how will you preserve and make your data available to others?

How It Works

From NSF’s Data Management Plan Guidelines:

  1. the types of data, samples, physical collections, software, curriculum materials, and other materials to be produced in the course of the project;
  2. the standards to be used for data and metadata format and content (where existing standards are absent or deemed inadequate, this should be documented along with any proposed solutions or remedies);
  3. policies for access and sharing including provisions for appropriate protection of privacy, confidentiality, security, intellectual property, or other rights or requirements;
  4. policies and provisions for re-use, re-distribution, and the production of derivatives;
  5. plans for archiving data, samples, and other research products, and for preservation of access to them.

Overview of Best Practices



Documentation with the Open Science Framework

  • Wiki: document your lab procedures, standards, etc.
  • Collaborators: add collaborators of all levels, on different parts of your project
  • Components: sub-projects to organize your research
  • Version Control: upload files of the same name & OSF will track your versions!
  • Add-Ons: use OSF to bring together tools you use | GitHub
  • Registrations: when you have an unchanging version of your project, register it & get a DOI!

Documentation with
Jupyter Notebooks

  1. Web Application
    • in-browser code editing: syntax highlighting & indentation
    • run code in-browser: results attached to parent code
    • display results in LaTex, HTML, SVG, & more
  2. Notebook
    • a complete record of a session, interleaving code with text, maths, & objects
    • can export to LaTex, PDF, slideshows, etc. or webpage

Basically, think to yourself:

if I wanted to use this data in 10 years, what would I need to pack with it to make it useful?

Keep all those things

Documenting Local Files

Documenting Local Files

Bulk File Renaming Generating READMEs
Concise with no special characters!

Windows: Rename-It! & rename.bat
Mac: NameChanger & readme_maker_MAC
A readme file is a .txt, .xml., or .html file that sits in a directory & explains the context and uses of each files in that same directory.

Windows: toHTML.bat, toCSV.bat, & toTXT.bat

Mac: file_rename_mac

Storage Rules!

NYU Storage Resources

  NYU Google Drive NYU Box
Intended use Personal archive not including sensitive or secure data Departmental & personal research with a focus on sensitive or secure data
Storage size Unlimited Unlimited
Sharing and user control Yes Yes
Versioning and file change tracking Some Yes

Anonymizing Data

  • Anonymizing data:
    • Direct identifiers (name, DOB, SSN, address, id numbers, etc.)
    • Indirect identifiers (variables in combination that enable identification)

  • Solutions:
    • Removal of identifying variables
    • Binning values/top coding (i.e. hide unique outlier values or aggregate values)
    • Disturbing (add random values to encoded value, retaining integrity of statistical accuracy)

Long Term Storage

Choose what you want to preserve/get to in the long term, but No matter WHAT, make sure you keep:

  • documentation (lab/field notebooks, etc.)
  • tools & analysis
Put your data into an archival format!

  • this should be open + accessible
  • Software agnostic

Archival Storage in Repositories

When you publish, you should make the underlying data available in a repository that issues DOIs! You then link that DOI in your "Supplementary Materials" section!

This means that anyone who wants to use your data must go to this repository, download it, and cite their use if they publish using it!

Example: Dryad Data Repository

Advantages to Tracking Citations:

  • Demonstrate to funders/promotion committees you & your data make big impacts in your field!
    • they judge merit based on intellectual merit and wider impact
    • tangible evidence to weigh against the cost of research

  • Monitor usage of datasets!
    • You can know what forms of data prep and data publication are most effective for sharing/open science!
    • Uncover opportunities for collaboration amongst peers

Getting Credit for Your Data

Example Data Management Plans

DMP Resources at NYU

The DMPTool provides templates & step-by-step guidance on most granting agencies. NYU affiliates can sign into the DMP Tool using their netID and password from the institutional log-in page.

Thank you! Questions?

Email us: vicky.steeves@nyu.edu & nicholas.wolf@nyu.edu

Learn more about RDM: guides.nyu.edu/data_management

Get this presentation: guides.nyu.edu/data_management/resources

Make an appointment: guides.nyu.edu/appointment